Advancements in data processing, data storage, and other digital technologies have facilitated the collection and storage of large amounts of data. The data, once collected and stored, is available for subsequent processing and analysis.
The collection and use of data is widespread, used throughout many different types of business, and other, enterprises for many varied purposes, and the same, or different, data is collected and processed for many different functions within a single enterprise. With continued advancements, yet greater capabilities by which to store and process data shall likely be available, permitting even greater amounts of data to be processed and stored pursuant to existing and new functions.
While ever-greater amounts of data are collected and processed, the data, to be useful, must be presentable in a form permitting ready interpretation of the data. With the availability of increased amounts of data, challenges associated with the presentation of the data in a form to permit its ready interpretation becomes greater.
In many presentation scenarios, data is displayed upon a computer-terminal screen, or analogous display, and an operator views the displayed data. The data before, and after, processing is sometimes in the form of data strings, sometimes large numbers of different data strings. Simple display of the data strings are difficult, even when placed in spreadsheet form, to be readily understood. As the number of data streams might be large, that is, of a large number of dimensions, the display of the data in mere numeric, or even spreadsheet, form is generally a relatively poor manner of displaying the data to permit its ready identification and interpretation.
Significant attention has been directed towards providing manners by which better to display data in a form to permit its ready interpretation. Painterly visualization techniques, for example, provide for the visualization of data by utilizing data values to alter images, and the alteration of the images, when viewed, are more readily noticeable. For instance, Herman Chernoff, in 1973, introduced a visualization technique to facilitate at least trends, i.e., changes in, multi-dimensional data. Simple, facial images are used in the visualization technique. And, facial features of the facial images are changed, depending upon changes in the data. Different data dimensions are mapped to different facial features. For example, the width of the face of the image, the location of the ears at the image, the radius of the ears, the length or curvature of the mouth, e.g., smiling or frowning, the length of the nose, etc. are each alterable. The features selected for display as part of the facial image represent trends in the values of the data of the various dimensions. Even though specific values are not, themselves, displayed, identification of the trends by an operator facilitate determination of sections of the data that are of particular interest. Ten facial characteristic parameters are identified in the Chernoff visualization technique. The head eccentricity, the eye eccentricity, the pupil size, the eyebrow slant, the nose size, the mouth shape, eye spacing, the eye size, the mouth length, and the degree of mouth opening are each facial characteristic parameters that are alterable responsive to values of the different dimensions of the data. Each parameter is represented by a number between zero and one.
A further technique is provided by Flury and Ridewyl. In this technique, multivariate data is displayed using Chernoff-like facial images in which the symmetry of the images are reduced, and eighteen parameters are provided for each side of the facial image. Specifically, the eighteen parameters include a pupil position, an eye slant, a pupil size, an eyebrow slant, a horizontal position of the eye, a vertical position of the eye, a curvature of the eye brow, a density of the eye brow, a horizontal position of the eye brow, a vertical position of the eye brow, an upper hair line, a lower hair line, a face line, the darkness of the hair, the hair shading slant, the eye size, the nose, and the size of the mouth in the image. While providing better for the visualization of multi-dimensional data, the dimensionality permitted in the existing visualization techniques and the aesthetic quality of the visualizations are generic.
If an improved visualization could be provided, better, and quicker, interpretation of the data would be possible.
It is in light of this background information relating to the display of data that the significant improvements of the present invention have evolved.